Completed Food & Agriculture Engineering

GCRF Development Corridors Partnership (DCP)

In plain English

AI plain-English summary

Development corridors in eastern Africa—planned highways, ports, and agricultural zones—are being built through lands that are already home to millions of people, yet the people who make decisions about these projects often lack the data and local expertise to manage them well. This matters because these corridors, such as Tanzania’s SAGCOT and Kenya’s LAPSSET, are meant to drive economic growth but currently risk worsening inequality, displacing communities, and failing under climate pressures. The problem is not a lack of investment, but a lack of research capacity in the region to generate evidence that decision-makers can actually use. The project brings together researchers from eastern Africa, China, and the UK with conservation groups and government agencies to produce that evidence—on land use, climate vulnerability, and social impacts—and feed it directly into planning processes. If successful, it could shift how billions of dollars in infrastructure investment are designed, making them more sustainable and equitable. The work is not fundamental science; it is applied, policy-targeted research aimed at changing how decisions get made on the ground.

View original technical description
The concept of 'development corridors' is increasingly used to support economic growth in Africa, driven by international as well as national interests. Development corridors have tremendous development potential yet they face significant challenges. These include uneven development impacts, traversing so-called "underutilised" lands that are generally already populated and managed, and vulnerability to climate change. Such challenges result in a lack of appropriate research capacity in the region. This proposal aims to addresses these challenges through engagement with decision makers and by developing relevant capacity within research institutions and researchers in eastern Africa, China and the UK. The research is targeted to generate decision-relevant evidence and feed it into key decision making processes in order to improve the sustainable development outcomes of investments in development corridors. The proposal is focused on corridors in eastern Africa, particularly the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) and the Lamu Port and Lamu-Southern Sudan-Ethiopia Transport Corridor (LAPSSET) in Kenya. The consortium is led by the United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC), who would be contracted as 'WCMC', and comprises five universities (Cambridge, London School of Economics, Nairobi, Sokoine University of Agriculture and York) and three boundary agents (World-Wide Fund for Nature (Tanzania), African Conservation Centre (ACC) and the China National Centre for Climate Change Strategy and International Cooperation (NCSC) of the National Development and Reform Commission (NDRC). The work is structured around three outcomes and six Work Packages, fully integrating research and capacity development, and significant policy engagement and outreach.

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Researchers

Christopher Sandbrook (Co-Investigator)Daniel Olago (Co-Investigator)Declan Conway (Co-Investigator)Elizabeth Watson (Co-Investigator)Japhet Joel Kashaigili (Co-Investigator)Neil Burgess (Principal Investigator)Pantaleo Munishi (Co-Investigator)Rob Marchant (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

GCRF-AFRICAP - Agricultural and Food-system Resilience: Increasing Capacity and Advising Policy
Balancing ecological integrity and infrastructure development: Optimizing the UK's contribution to Sustainable Development Goals in sub-Sahara Africa
NCEO NC ODA Full
GCRF: Social and Environmental Trade-offs in African Agriculture
GCRF - Building REearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BRECcIA)"

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.